A market-oriented incentive mechanism for emergency demand response in colocation data centers

Abstract Rapidly developing colocation data centers (or colocations, for short) have become important participants in emergency demand response (EDR) programs. Different from traditional data centers, in colocations, tenants control their own servers; thus, they may not coordinate to reduce their power consumption. In this paper, to encourage tenants to join EDR programs, we propose a market-oriented incentive mechanism, MicDR, which can effectively reduce energy costs. MicDR includes a local incentive mechanism (LiMec), a global incentive mechanism (GiMec) and a server-sharing incentive mechanism (SiMec). LiMec motivates tenants to improve their energy efficiency locally. GiMec encourages tenants to improve their energy efficiency by requesting public server resources. To support the requests sparked by GiMec, SiMec encourages tenants to share idle server resources. A (1 + ϵ)-approximation algorithm is proposed to achieve an asymptotic optimal energy-saving scheme. The performance of the proposed algorithm is evaluated, and trace-driven simulations verify the effectiveness and feasibility of MicDR.

[1]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[2]  Mohsen Guizani,et al.  Shaving Data Center Power Demand Peaks Through Energy Storage and Workload Shifting Control , 2019, IEEE Transactions on Cloud Computing.

[3]  Shaolei Ren,et al.  Energy efficiency in colocation data centers: A joint incentive mechanism approach , 2017, 2017 Eighth International Green and Sustainable Computing Conference (IGSC).

[4]  Girish Ghatikar,et al.  Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies , 2012 .

[5]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[6]  Miao Pan,et al.  Coordinated energy management for colocation data centers in smart grids , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[7]  Adam Wierman,et al.  Greening Multi-Tenant Data Center Demand Response , 2015, PERV.

[8]  Di Wang,et al.  Leveraging energy storage to optimize data center electricity cost in emerging power markets , 2016, e-Energy.

[9]  Shaolei Ren,et al.  A truthful incentive mechanism for emergency demand response in colocation data centers , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[10]  Zhiyong Liu,et al.  Truthful Strategy and Resource Integration for Multi-tenant Data Center Demand Response , 2015, FAW.

[11]  Costas Courcoubetis,et al.  Pricing communication networks - economics, technology and modelling , 2003, Wiley-Interscience series in systems and optimization.

[12]  Rui Wang,et al.  Real-time Task Scheduling for joint energy efficiency optimization in data centers , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[13]  Adam Wierman,et al.  Opportunities and challenges for data center demand response , 2014, International Green Computing Conference.

[14]  Shaolei Ren,et al.  Colocation Demand Response: Why Do I Turn Off My Servers? , 2014, ICAC.

[15]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[16]  Tao Jiang,et al.  Energy Cost Minimization for Distributed Internet Data Centers in Smart Microgrids Considering Power Outages , 2015, IEEE Transactions on Parallel and Distributed Systems.

[17]  Adam Wierman,et al.  A market approach for handling power emergencies in multi-tenant data center , 2016, 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[18]  Yuguang Fang,et al.  Electricity Cost Saving Strategy in Data Centers by Using Energy Storage , 2013, IEEE Transactions on Parallel and Distributed Systems.

[19]  Sergio Vazquez,et al.  Flexible and cost effective hybrid energy storage system based on batteries and ultracapacitors , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[20]  Jan Broeckhove,et al.  Black box scheduling for resource intensive virtual machine workloads with interference models , 2013, Future Gener. Comput. Syst..

[21]  Shaolei Ren,et al.  A Contract design approach for colocation data center demand response , 2015, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[22]  Miao Pan,et al.  A Nash Bargaining Approach to Emergency Demand Response in Colocation Data Centers , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[23]  Athanasios V. Vasilakos,et al.  GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks , 2013, IEEE Journal on Selected Areas in Communications.

[24]  Konstantinos Poularakis,et al.  Mobile Data Offloading Through Caching in Residential 802.11 Wireless Networks , 2016, IEEE Transactions on Network and Service Management.

[25]  Sujata Banerjee,et al.  ElasticTree: Saving Energy in Data Center Networks , 2010, NSDI.

[26]  E. H. Clarke Multipart pricing of public goods , 1971 .

[27]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[28]  Deshi Ye,et al.  A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers , 2015, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[29]  Shaolei Ren,et al.  TECH: A Thermal-Aware and Cost Efficient Mechanism for Colocation Demand Response , 2016, 2016 45th International Conference on Parallel Processing (ICPP).

[30]  Eui-Nam Huh,et al.  Reward-to-Reduce: An Incentive Mechanism for Economic Demand Response of Colocation Datacenters , 2016, IEEE Journal on Selected Areas in Communications.

[31]  Tim Roughgarden,et al.  Stackelberg scheduling strategies , 2001, STOC '01.

[32]  Shaolei Ren,et al.  Paying to save: Reducing cost of colocation data center via rewards , 2015, 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA).

[33]  Zhu Han,et al.  Incentive Mechanisms for Economic and Emergency Demand Responses of Colocation Datacenters , 2015, IEEE Journal on Selected Areas in Communications.